Local Distance Functions: A Taxonomy, New Algorithms, and an Evaluation
We present a taxonomy for local distance functions where
most existing algorithms can be regarded as approximations of
the geodesic distance defined by a metric tensor. We categorize
existing algorithms by how, where, and when they estimate the
metric tensor. We also extend the taxonomy along each axis.
How: We introduce hybrid algorithms that use a combination
of techniques to ameliorate over-fitting. Where: We present an
exact polynomial time algorithm to integrate the metric tensor
along the lines between the test and training points under the
assumption that the metric tensor is piecewise constant. When:
We propose an interpolation algorithm where the metric tensor
is sampled at a number of references points during the offline
phase. The reference points are then interpolated during the
online classification phase. We also present a comprehensive
evaluation on tasks in face recognition, object recognition, and
digit recognition.
Download: pdf
Text Reference
Deva Ramanan and Simon Baker. Local distance functions: a taxonomy, new algorithms, and an evaluation. In IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI). 2011.BibTeX Reference
@inproceedings{RamananB_PAMI_2011,author = "Ramanan, Deva and Baker, Simon",
booktitle = "IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI)",
title = "Local Distance Functions: A Taxonomy, New Algorithms, and an Evaluation",
year = "2011",
tag = "object_recognition"
}